| pcaivortho {ade4} | R Documentation |
performs a Principal Component Analysis with respect to orthogonal instrumental variables.
pcaivortho(dudi, df, scannf = TRUE, nf = 2)
dudi |
a duality diagram, object of class dudi |
df |
a data frame with the same rows |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
an object of class 'pcaivortho' sub-class of class dudi
rank |
an integer indicating the rank of the studied matrix |
nf |
an integer indicating the number of kept axes |
eig |
a vector with the all eigenvalues |
lw |
a numeric vector with the row weigths (from dudi) |
cw |
a numeric vector with the column weigths (from dudi) |
Y |
a data frame with the dependant variables |
X |
a data frame with the explanatory variables |
tab |
a data frame with the modified array (projected variables) |
c1 |
a data frame with the Pseudo Principal Axes (PPA) |
as |
a data frame with the Principal axis of dudi$tab on PAP |
ls |
a data frame with the projection of lines of dudi$tab on PPA |
li |
a data frame dudi$ls with the predicted values by X |
l1 |
a data frame with the Constraint Principal Components (CPC) |
co |
a data frame with the inner product between the CPC and Y |
param |
a data frame containing a summary |
Daniel Chessel
Anne B Dufour dufour@biomserv.univ-lyon1.fr
Rao, C. R. (1964) The use and interpretation of principal component analysis in applied research. Sankhya, A 26, 329–359.
Sabatier, R., Lebreton J. D. and Chessel D. (1989) Principal component analysis with instrumental variables as a tool for modelling composition data. In R. Coppi and S. Bolasco, editors. Multiway data analysis, Elsevier Science Publishers B.V., North-Holland, 341–352
## Not run:
par(mfrow = c(2,2))
data(avimedi)
cla <- avimedi$plan$reg:avimedi$plan$str
# simple ordination
coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3)
s.class(coa1$li, cla, sub = "Sans contrainte")
# within region
w1 <- within(coa1, avimedi$plan$reg, scan = FALSE)
s.match(w1$li, w1$ls, clab = 0, sub = "Intra Région")
s.class(w1$li, cla, add.plot = TRUE)
# no region the same result
pcaivnonA <- pcaivortho(coa1, avimedi$plan$reg, scan = FALSE)
s.match(pcaivnonA$li, pcaivnonA$ls, clab = 0,
sub = "Contrainte Non A")
s.class(pcaivnonA$li, cla, add.plot = TRUE)
# region + strate
interAplusB <- pcaiv(coa1, avimedi$plan, scan = FALSE)
s.match(interAplusB$li, interAplusB$ls, clab = 0,
sub = "Contrainte A + B")
s.class(interAplusB$li, cla, add.plot = TRUE)
par(mfrow = c(1,1))## End(Not run)